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In the past, bank fraud detection was accomplished using a rules engine, which went down a set of binary questions to arrive at a simple “yes or no” to the question: is this a fraudulent transaction? Today most banking is done online, and cybercriminals are using sophisticated ways to steal. And once an account is compromised, thieves need only a few minutes to steal thousands. Fraud detection must happen in near-real-time to be effective, and a rules engine will not suffice. Instead, new tools such as advanced data analytics are combined with machine learning to adapt to new threats. Data that seems innocuous can be used to determine if a user is real—questions such as "how frequently does this device or consumer log in?" can be used to spot fraud.
Our solution guide on mobile bank fraud walks through the challenge and the strategies that can be used to build a fraud-detection solution—one that answers yes or no in two seconds.